Adaptive sliding-mode type-2 neuro-fuzzy control of an induction motor

•We control an induction motor based on field oriented control.•The fusion of sliding-mode and type-2 neuro fuzzy systems is used.•Response of proposed controller in the presence of uncertainties is shown.•The results of this controller are compared with those of type-1 counterpart. An innovative ad...

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Bibliographic Details
Published in:Expert systems with applications 2015-11, Vol.42 (19), p.6635-6647
Main Authors: Masumpoor, Saleh, yaghobi, Hamid, Ahmadieh Khanesar, Mojtaba
Format: Article
Language:English
Subjects:
Adaptive control
Adaptive control systems
Algorithms
Artificial neural networks
Control systems
Controllers
Field oriented control
Fuzzy logic
Fuzzy systems
Induction motor
Induction motors
Sliding mode control
Type-2 neuro-fuzzy
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Summary:•We control an induction motor based on field oriented control.•The fusion of sliding-mode and type-2 neuro fuzzy systems is used.•Response of proposed controller in the presence of uncertainties is shown.•The results of this controller are compared with those of type-1 counterpart. An innovative adaptive control method for speed control of induction motor based on field oriented control is presented in this paper. The fusion of sliding-mode and type-2 neuro fuzzy systems is used to control this system. An online learning algorithm based on sliding-mode training algorithm, and type-2 fuzzy systems is employed to deal with parametric uncertainties and disturbances, by adjusting the control parameters. The sliding-mode adaptive mechanism tune the parameters of type-2 membership functions (antecedent part) and the consequent part parameters, according to the inputs: speed error and its derivative, in structure of type-2 neuro fuzzy system. Since the parameters of the induction motor may vary, and the information that is used to construct the membership functions and the rules of fuzzy logic system is uncertain, type-2 neuro fuzzy structure is selected as the controller. The results obtained by using this approach are compared with those of type-1 counterpart. The proposed adaptive sliding-mode type-2 neuro-fuzzy controller can control the induction motor with higher performance as it is compared with type-1 neuro-fuzzy systems while it shows more robustness to variations in the parameters and measurement noise.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2015.04.046